A machine-learning approach to modelling and forecasting the minimum temperature at Dhahran, Saudi Arabia

R.E. Abdel-Aal and M.A. Elhadidy

Energy, 1994, vol. 19, issue 7, 739-749

Abstract: We investigate the use of modern machine-learning techniques for weather prediction. The AIM‡‡AIM is a Registered Trademark of AbTech Corporation, Charlottesville, VA, U.S.A. (Abductory Induction Mechanism) tool for the Macintosh computer has been used for modelling and 3-day forecasting of the minimum temperature in the Dhahran region. Compared to other statistical methods and neural network techniques, this approach has the advantages that model synthesis is much faster and highly automated, requiring little or no user intervention. The resulting models are simpler, requiring data for fewer weather parameters, and prediction and forecasting accuracies are also superior. AIM models were developed using daily data for 18 weather parameters over one year to predict the minimum temperature from other parameters on the same day. Evaluated by using data for another full year, the models give over 99% yield in the ± 3 °C error category compared to approximately 67% for both a statistical model and a model based on back propagation artificial neural networks. AIM forecasting models give a corresponding yield of 93% for the first forecasting day. Model relationships describing the synthesized AIM networks are compared with those derived through a statistical model previously developed for the region. The effect of increasing the model complexity is investigated for both modelling and forecasting.

Date: 1994
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:19:y:1994:i:7:p:739-749

DOI: 10.1016/0360-5442(94)90012-4

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